ERC FUNDED PROJECTS

ProjectActively Enhanced Cognition based Framework for Design of Complex Systems

Researcher (PI)Björn Ottersten

Host Institution (HI)UNIVERSITE DU LUXEMBOURG

Call DetailsAdvanced Grant (AdG), PE7, ERC-2016-ADG

SummaryParameterized mathematical models have been central to the understanding and design of communication, networking, and radar systems. However, they often lack the ability to model intricate interactions innate in complex systems. On the other hand, data-driven approaches do not need explicit mathematical models for data generation and have a wider applicability at the cost of flexibility. These approaches need labelled data, representing all the facets of the system interaction with the environment. With the aforementioned systems becoming increasingly complex with intricate interactions and operating in dynamic environments, the number of system configurations can be rather large leading to paucity of labelled data. Thus there are emerging networks of systems of critical importance whose cognition is not effectively covered by traditional approaches. AGNOSTIC uses the process of exploration through system probing and exploitation of observed data in an iterative manner drawing upon traditional model-based approaches and data-driven discriminative learning to enhance functionality, performance, and robustness through the notion of active cognition. AGNOSTIC clearly departs from a passive assimilation of data and aims to formalize the exploitation/exploration framework in dynamic environments. The development of this framework in three applications areas is central to AGNOSTIC. The project aims to provide active cognition in radar to learn the environment and other active systems to ensure situational awareness and coexistence; to apply active probing in radio access networks to infer network behaviour towards spectrum sharing and self-configuration; and to learn and adapt to user demand for content distribution in caching networks, drastically improving network efficiency. Although these cognitive systems interact with the environment in very different ways, sufficient abstraction allows cross-fertilization of insights and approaches motivating their joint treatment.

Parameterized mathematical models have been central to the understanding and design of communication, networking, and radar systems. However, they often lack the ability to model intricate interactions innate in complex systems. On the other hand, data-driven approaches do not need explicit mathematical models for data generation and have a wider applicability at the cost of flexibility. These approaches need labelled data, representing all the facets of the system interaction with the environment. With the aforementioned systems becoming increasingly complex with intricate interactions and operating in dynamic environments, the number of system configurations can be rather large leading to paucity of labelled data. Thus there are emerging networks of systems of critical importance whose cognition is not effectively covered by traditional approaches. AGNOSTIC uses the process of exploration through system probing and exploitation of observed data in an iterative manner drawing upon traditional model-based approaches and data-driven discriminative learning to enhance functionality, performance, and robustness through the notion of active cognition. AGNOSTIC clearly departs from a passive assimilation of data and aims to formalize the exploitation/exploration framework in dynamic environments. The development of this framework in three applications areas is central to AGNOSTIC. The project aims to provide active cognition in radar to learn the environment and other active systems to ensure situational awareness and coexistence; to apply active probing in radio access networks to infer network behaviour towards spectrum sharing and self-configuration; and to learn and adapt to user demand for content distribution in caching networks, drastically improving network efficiency. Although these cognitive systems interact with the environment in very different ways, sufficient abstraction allows cross-fertilization of insights and approaches motivating their joint treatment.

SummarySoftware-intensive systems pervade modern society and industry. These systems often play critical roles from an economic, safety or security standpoint, thus making their dependability indispensible. Software Verification and Validation (V&V) is core to ensuring software dependability. The most prevalent V&V technique is testing, that is the automated, systematic, and controlled execution of a system to detect faults or to show compliance with requirements. Increasingly, we are faced with systems that are untestable, meaning that traditional testing methods are highly expensive, time-consuming or infeasible to apply due to factors such as the systems’ continuous interactions with the environment and the deep intertwining of software with hardware.
TUNE will enable testing of untestable systems by revolutionising how we think about test automation. Our key idea is to frame testing on models rather than operational systems. We refer to such testing as model testing. The models that underlie model testing are executable representations of the relevant aspects of a system and its environment, alongside the risks of system failures. Such models inevitably have uncertainties due to complex, dynamic environment behaviours and the unknowns about the system. This necessitates that model testing be uncertainty-aware.
We propose to develop scalable, practical and uncertainty-aware techniques for test automation, leveraging our expertise on model-driven engineering and automated testing. Our solutions will synergistically combine metaheuristic search with system and risk models to drive the search for critical faults that entail the most risk. TUNE is the first initiative with the specific goal of raising the level of abstraction of testing from operational systems to models. The project will bring early and cost-effective automation to the testing of many critical systems that defy existing automation techniques, thus significantly improving the dependability of such systems.

Software-intensive systems pervade modern society and industry. These systems often play critical roles from an economic, safety or security standpoint, thus making their dependability indispensible. Software Verification and Validation (V&V) is core to ensuring software dependability. The most prevalent V&V technique is testing, that is the automated, systematic, and controlled execution of a system to detect faults or to show compliance with requirements. Increasingly, we are faced with systems that are untestable, meaning that traditional testing methods are highly expensive, time-consuming or infeasible to apply due to factors such as the systems’ continuous interactions with the environment and the deep intertwining of software with hardware.
TUNE will enable testing of untestable systems by revolutionising how we think about test automation. Our key idea is to frame testing on models rather than operational systems. We refer to such testing as model testing. The models that underlie model testing are executable representations of the relevant aspects of a system and its environment, alongside the risks of system failures. Such models inevitably have uncertainties due to complex, dynamic environment behaviours and the unknowns about the system. This necessitates that model testing be uncertainty-aware.
We propose to develop scalable, practical and uncertainty-aware techniques for test automation, leveraging our expertise on model-driven engineering and automated testing. Our solutions will synergistically combine metaheuristic search with system and risk models to drive the search for critical faults that entail the most risk. TUNE is the first initiative with the specific goal of raising the level of abstraction of testing from operational systems to models. The project will bring early and cost-effective automation to the testing of many critical systems that defy existing automation techniques, thus significantly improving the dependability of such systems.